疲劳驾驶检测代码c++

以下是一个简单的疲劳驾驶检测代码，使用C++编写：

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main()
{
    // 打开摄像头
    VideoCapture cap(0);
    if (!cap.isOpened()) {
        cout << "无法打开摄像头！" << endl;
        return -1;
    }

    // 加载人脸检测器模型
    CascadeClassifier face_cascade;
    if (!face_cascade.load("haarcascade_frontalface_alt.xml")) {
        cout << "无法加载人脸检测器模型！" << endl;
        return -1;
    }

    // 创建窗口
    namedWindow("疲劳驾驶检测");

    while (true) {
        Mat frame;
        cap >> frame;  // 读取一帧图像

        // 检测人脸
        vector<Rect> faces;
        face_cascade.detectMultiScale(frame, faces, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30));

        bool is_sleepy = false;
        for (size_t i = 0; i < faces.size(); i++) {
            Rect face = faces[i];
            rectangle(frame, face, Scalar(0, 0, 255), 2);

            // 计算眼睛区域
            Rect eye_region_left(face.x + face.width / 16,
                                  face.y + (face.height / 4.5),
                                  (face.width - 2 * face.width / 16) / 2,
                                  (face.height / 3));
            Rect eye_region_right(face.x + face.width / 16 + (face.width - 2 * face.width / 16) / 2,
                                   face.y + (face.height / 4.5),
                                   (face.width - 2 * face.width / 16) / 2,
                                   (face.height / 3));

            // 在眼睛区域内检测瞳孔
            Mat eye_left = frame(eye_region_left);
            Mat eye_right = frame(eye_region_right);

            // 灰度化
            Mat gray_left, gray_right;
            cvtColor(eye_left, gray_left, COLOR_BGR2GRAY);
            cvtColor(eye_right, gray_right, COLOR_BGR2GRAY);

            // 直方图均衡化
            equalizeHist(gray_left, gray_left);
            equalizeHist(gray_right, gray_right);

            // 检测瞳孔
            vector<Vec3f> circles_left, circles_right;
            HoughCircles(gray_left, circles_left, HOUGH_GRADIENT, 1, gray_left.rows / 8, 200, 100, 0, 0);
            HoughCircles(gray_right, circles_right, HOUGH_GRADIENT, 1, gray_right.rows / 8, 200, 100, 0, 0);

            // 计算瞳孔位置
            Point center_left, center_right;
            int radius_left, radius_right;
            if (circles_left.size() > 0) {
                center_left = Point(cvRound(circles_left[0][0]), cvRound(circles_left[0][1]));
                radius_left = cvRound(circles_left[0][2]);
                circle(eye_left, center_left, radius_left, Scalar(0, 0, 255), 2);
            }
            if (circles_right.size() > 0) {
                center_right = Point(cvRound(circles_right[0][0]), cvRound(circles_right[0][1]));
                radius_right = cvRound(circles_right[0][2]);
                circle(eye_right, center_right, radius_right, Scalar(0, 0, 255), 2);
            }

            // 判断是否闭眼
            if (circles_left.size() == 0 || circles_right.size() == 0) {
                is_sleepy = true;
            }
            else {
                int min_radius = min(radius_left, radius_right);
                int max_radius = max(radius_left, radius_right);
                double radius_ratio = (double)min_radius / max_radius;
                if (radius_ratio < 0.4) {
                    is_sleepy = true;
                }
            }
        }

        // 显示图像
        imshow("疲劳驾驶检测", frame);

        // 检测到闭眼则播放警报声
        if (is_sleepy) {
            cout << "警报！疲劳驾驶！" << endl;
            // TODO: 播放警报声
        }

        // 等待按下ESC退出程序
        if (waitKey(30) == 27) {
            break;
        }
    }

    return 0;
}

这个代码使用了OpenCV库，利用摄像头实时检测人脸并在人脸区域内检测瞳孔，判断瞳孔大小比例是否过小，从而判断是否闭眼，如果检测到闭眼则播放警报声。需要注意的是，本代码仅供参考，实际应用时需要根据具体情况进行修改和优化。